Project summary Key biophysical properties such as drug binding sites and enzyme catalysis arise can be computer-modeled using quantum mechanics, but limitations in the accuracy of practical quantum methods have held back progress. Over the past five years, this situation has changed with exciting, (and ongoing) improvements in the accuracy of density functional theory (DFT). New and better density functionals open new opportunities for applications in conformational searching, molecular recognition, ligand binding, and all the areas where ab initio calculations are employed in biophysical chemistry. However, these functionals require very large and computationally demanding basis sets to attain their high accuracy. Use of smaller basis sets leads to unconverged results with often unacceptable errors. There is an unmet need to significantly reduce the computational cost of achieving large basis set accuracy. The central innovation of this proposal is to use minimal adaptive basis functions (MAB) for this purpose, in place of traditional large basis sets. The MAB is a small (minimal) set of functions, adaptively formed from a traditional large basis via an atom-blocked, sparse transformation. The DFT calculation is performed in the adaptive basis, followed by a dual basis correction. This potentially permits very large computational speedups, while yielding accuracy virtually indistinguishable from a computationally costly calculation performed conventionally in the large target basis. The Phase I research has three principal objectives. First, the research will establish the accuracy of the MAB protocol for a range of biophysically relevant energy differences. Second, the research will lead to a carefully justified estimate of the speed-up that is attainable with the MAB approach, and will produce a new software implementation of several of the algorithmic steps that must be optimized. Third, modifications and improvements of the MAB approach will be sought as possible and needed. The results will lay the groundwork for basis set limit DFT calculations at greatly reduced computational cost, thereby potentially greatly expanding their usefulness for biophysical modeling.